But there are two parts to machine learning. There is a train/test part, where you use a lot of data to build a model. And there’s deployment, where you take a model and use it as part of a project. And that’s where the Raspberry Pi fits in.Īlthough Raspberry Pi isn’t officially supported by Google, there are example models included for the Raspberry Pi and it can be fun to get TensorFlow up and running on a Pi. And there are lots of interesting community projects around that put TensorFlow to good use. Using TensorFlow can give you a good understanding of how AI works, and how to put AI to practical use in your projects. #How To Install Mplayer Raspberry Pi how to Hopefully, you now have TensorFlow up and running. Hello = tf.constant('Hello, TensorFlow!') Choose File > New File and enter the hello_tensorflow.py code: import tensorflow as tf Open Python 3 (IDLE) using Menu > Programming > Python 3 (IDLE). Save the code file as hello_tensorflow.py and Choose Run > Run Module. #How To Install Mplayer Raspberry Pi code You will get a warning because TensorFlow is compiled for Python 3.4 and we’re running Python 3.5. Google has a bunch of models developed for Raspberry Pi that you can test out. Start by cloning the TensorFlow repository: git clone įollow the instructions here to build the example models. Now head to the part of the TensorFlow repository to find Google example models and instructions. The default example is a picture of Grace Hopper. Run it and you will see that it identifies a ‘military uniform’, ‘suit’, and ‘academic gown’ (and then other items in order of decreasing probability). From here you can see how this model could be used to identify objects in your own images, and use that in your own code. There is also a link to an example that uses the Pi Camera Module directly. Now you have everything you need to start using TensorFlow. It’s a big subject and there’s far more to it than we could outline in this tutorial (or even this entire magazine). Learn by doing and follow some TensorFlow projects. Start with Sarthak Jain’s ‘How to easily detect objects with deep learning on Raspberry Pi’ or Alasdair Allan’s ‘Magic mirror with TensorFlow’. You can try to build TensorFlow using the wheel file. #How To Install Mplayer Raspberry Pi downloadĭownload the wheel file and run it, like this: sudo pip3 install -upgrade tensorflow-1.9.0rc0-cp34-none-linux_armv7l.whl In a Terminal, enter: sudo pip3 install -upgrade Īlternatively, you can use a nightly wheel built for Raspberry Pi, which is available from /xKLBzu. #How To Install Mplayer Raspberry Pi code.#How To Install Mplayer Raspberry Pi software.#How To Install Mplayer Raspberry Pi how to.Producing special versions which break other standard programs of Raspbian is not a good idea. The last ffmpeg version (before this update) was also specially compiled for the RPi (it came from the Foundation repository) and this version worked with mplayer. Smplayer's status bar options can be configured to show among other things milliseconds which I would imagine would be useful for video editing. I know that once ffmpeg was compiled the next step compiling mpv even on the pi 3 was a relatively quick and I presume compiling mplayer againt raspbian's ffmpeg would not be too onerous. I remember when I compiled my own ffmpeg for my pi 3b when I compiled mpv for version 29 I had to explicitly disable vdpau to get it to work. As you are no doubt aware the version of ffmpeg in Raspbian's buster repository is not stock debian but has been compiled specifically for the Raspberry Pi and you will note that in its configuration it explicitly disabled vdpau. Its funny I just posted a reply which seems to have vanished into the ether so I will try again.
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